Audio Barlow Twins: Self-Supervised Audio Representation Learning
Jonah Anton, Harry Coppock, Pancham Shukla, Bjorn W. Schuller
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ReproduceCode
- github.com/jonahanton/ssl_audioOfficialIn paperpytorch★ 9
Abstract
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we present Audio Barlow Twins, a novel self-supervised audio representation learning approach, adapting Barlow Twins to the audio domain. We pre-train on the large-scale audio dataset AudioSet, and evaluate the quality of the learnt representations on 18 tasks from the HEAR 2021 Challenge, achieving results which outperform, or otherwise are on a par with, the current state-of-the-art for instance discrimination self-supervised learning approaches to audio representation learning. Code at https://github.com/jonahanton/SSL_audio.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| FSD50K | [ABT] AudioNTT | mAP | 0.47 | — | Unverified |